How many training example text classifier needs to be trained?
As a general rule of thumb I typically use 10*(# of features) for shallow machine learning models such as Naive Bayes with only 2 classes. 

So it all depends on the number of features you will be using. However, the more output classes the more data you will need for proper discrimination. The addition of more classes is not linear but I think you can get away with: 10*(# of features)*(# of output classes)